1,460,889 research outputs found
Visual parameter optimisation for biomedical image processing
Background: Biomedical image processing methods require users to optimise input parameters to ensure high quality
output. This presents two challenges. First, it is difficult to optimise multiple input parameters for multiple
input images. Second, it is difficult to achieve an understanding of underlying algorithms, in particular, relationships
between input and output.
Results: We present a visualisation method that transforms users’ ability to understand algorithm behaviour by
integrating input and output, and by supporting exploration of their relationships. We discuss its application to a
colour deconvolution technique for stained histology images and show how it enabled a domain expert to
identify suitable parameter values for the deconvolution of two types of images, and metrics to quantify
deconvolution performance. It also enabled a breakthrough in understanding by invalidating an underlying
assumption about the algorithm.
Conclusions: The visualisation method presented here provides analysis capability for multiple inputs and outputs
in biomedical image processing that is not supported by previous analysis software. The analysis supported by our
method is not feasible with conventional trial-and-error approaches
Identification of partial differential equation models for a class of multiscale spatio-temporal dynamical systems
In this paper, the identification of a class of multiscale spatio-temporal dynamical sys-tems, which incorporate multiple spatial scales, from observations is studied. The proposed approach is a combination of Adams integration and an orthogonal least squares algorithm, in which the multiscale operators are expanded, using polynomials as basis functions, and the spatial derivatives are estimated by finite difference methods. The coefficients of the polynomials can vary with respect to the space domain to represent the feature of multiple scales involved in the system dynamics and are approximated using a B-spline wavelet multi-resolution analysis (MRA). The resulting identified models of the spatio-temporal evolution form a system of partial differential equations with different spatial scales. Examples are provided to demonstrate the efficiency of the proposed method
Critical comments on EEG sensor space dynamical connectivity analysis
Many different analysis techniques have been developed and applied to EEG
recordings that allow one to investigate how different brain areas interact.
One particular class of methods, based on the linear parametric representation
of multiple interacting time series, is widely used to study causal
connectivity in the brain. However, the results obtained by these methods
should be interpreted with great care. The goal of this paper is to show, both
theoretically and using simulations, that results obtained by applying causal
connectivity measures on the sensor (scalp) time series do not allow
interpretation in terms of interacting brain sources. This is because 1) the
channel locations cannot be seen as an approximation of a source's anatomical
location and 2) spurious connectivity can occur between sensors. Although many
measures of causal connectivity derived from EEG sensor time series are
affected by the latter, here we will focus on the well-known time domain index
of Granger causality (GC) and on the frequency domain directed transfer
function (DTF). Using the state-space framework and designing two simulation
studies we show that mixing effects caused by volume conduction can lead to
spurious connections, detected either by time domain GC or by DTF. Therefore,
GC/DTF causal connectivity measures should be computed at the source level, or
derived within analysis frameworks that model the effects of volume conduction.
Since mixing effects can also occur in the source space, it is advised to
combine source space analysis with connectivity measures that are robust to
mixing
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